With over 2.4 million non-retail inside sales representatives in the United States (U.S.) alone, millions of sales phone conversations are made on a daily basis).i However, except for rudimentary statistics concerning e.g., call length and spotted keywords and phrases, sales conversations are left largely unanalyzed, rendering their content inaccessible to modeling, and precluding the ability to optimize them for desired outcomes. Recent advances in automatic speech recognition (ASR) technologies, and specifically in large vocabulary continuous speech recognition (LVCSR), are for the first time enabling high-accuracy automatic transcription of conversations. At the same time, natural language processing (NLP) approaches to both topic modeling and world-knowledge modeling, have become much more efficient due to the availability of large, freely accessible natural language corpora (e.g., CommonCrawl), as well as freely available ontologies or “knowledge graphs” (e.g., DBpedia). Finally, recent research on affect identification applying machine learning (ML) has been able to successfully model subjective aspects of emotion and personality traits as perceived by listeners.